Smart Grids - Electricals 4 Youe4uhu.com/down/smart grid technology/Sildes ( Dr.Majdi ).pdf · Smart Grids Definitions II In “Smarter Grids: The Opportunity”, the Smart Grid is

Smart Grids

EE 110409563

Dr. Majdi Alomari

The Hashemite University (HU)

February, 2015

Dr. Majdi Alomari The Hashemite University (HU)

Outline

Course Contents:

* Introduction.

* Components.

* Micro Grid.

* State Estimation.

* Renewable Resources.

* Cyber Security.

* Interfaces and Decision Support.

* Control Methods and Topologies.

* Trading in Smart Grid.


Outline

Purpose of this course

The aim of this course is:

• Understand the main challenges due to smart grid implementation.

• Detect the main components of electrical smart grids.

• Evaluate state estimation enhancement.

• Analyse the renewable energy resources models.

• Understand the cyber security problem.


Outline

Textbook:

Smart Grid: Fundamentals of Design and Analysis, by James Momoh,

John Wiley & Sons, Mar 20, 2012 - Technology & Engineering - 216

pages.


Introduction

What is Smart Grid?



Introduction

Introduction

Today Power Grid Capabilities

• Handling uncertainties in schedules and power

transfers across regions.

• Accommodating renewable.

• Optimizing the transfer capability of the transmission

and distribution networks and meeting the demand

for increased quality and reliable supply.

• Managing and resolving unpredictable events and

uncertainties in operations and planning more

aggressively.


Introduction

Today’s Grid vs. Smart Grid 1

1“The Modern Grid Initiative.” GridWise Architecture Council, Pacific Northwest National Laboratory, 2008 .

Preferred Characteristics Today’s Grid Smart Grid

Active Consumer Participation Consumers are uninformed and

do not participate

Informed, involved consumers

demand response and distributed

energy resources

Accommodation of all generation

and storage options

Dominated by central

generation many obstacles exist

for distributed energy resources

interconnection

Many distributed energy

resources with plug - and - play

convenience focus on renewables

New products, services, and

markets

Limited, poorly integrated

wholesale markets; limited

opportunities for consumers

Mature, well-integrated wholesale

markets; growth of new

electricity markets for consumers

Provision of power quality for the

digital economy

Focus on outages slow

response to power quality issues

Power quality a priority with a

variety of quality/price options

rapid resolution of issues

Optimization of assets and

operates efficiently

Little integration of operational

data with asset management -

business process silos

Greatly expanded data acquisition

of grid parameters; focus on

prevention, minimizing impact to

consumers

Anticipating responses to system

disturbances (self- healing)

Responds to prevent further

damage; focus on protecting

assets following a fault

Automatically detects and

responds to problems; focus on

prevention, minimizing impact to

consumers

Resiliency against cyber attack

and natural disasters

Vulnerable to malicious acts of

terror and natural disasters; slow

response

Resilient to cyber attack and

natural disasters; rapid

restoration capabilities


Dr. Majdi Alomari

Introduction


Introduction

Smart Grids Definitions I

The European Technology Platform defines the Smart Grid as:

A Smart Grid is an electricity network that can intelligently integrate

the actions of all users connected to it generators, consumers and

those that do both in order to efficiently deliver sustainable,

economic and secure electricity supplies.

According to the US Department of Energy: A smart grid uses

digital technology to improve reliability, security, and efficiency

(both economic and energy) of the electric system from large

generation, through the delivery systems to electricity consumers

and a growing number of distributed-generation and storage

resources.


Introduction

Smart Grids Definitions II

In “Smarter Grids: The Opportunity”, the Smart Grid is defined as:

A smart grid uses sensing, embedded processing and digital

communications to enable the electricity grid to be observable (able

to be measured and visualised), controllable (able to manipulated

and optimised), automated (able to adapt and self-heal), fully

integrated (fully interoperable with existing systems and with the

capacity to incorporate a diverse set of energy sources).


Dr. Majdi Alomari

A smart grid is a modernized electrical grid that uses analogue or

digital information and communications technology to gather and act on

information, such as information about the behaviours of suppliers and

consumers, in an automated fashion to improve the efficiency, reliability,

economics, and sustainability of the production and distribution of

electricity. (From Wikipedia)

A smart grid is an electricity network based on digital technology that is

used to supply electricity to consumers via two-way digital

communication. This system allows for monitoring, analysis, control and

communication within the supply chain to help improve efficiency, reduce

energy consumption and cost, and maximize the transparency and

reliability of the energy supply chain. The smart grid was introduced with

the aim of overcoming the weaknesses of conventional electrical grids by

using smart net meters. (from Techopedia)

Smart Grids Definitions

Introduction


Dr. Majdi Alomari

The Modern, Diverse Grid


Introduction

Dr. Majdi Alomari

Global Warming Mitigation Using Smart Micro-Grid


Introduction

Components

Evolution of Smart Metering

• The smart grid environment requires the upgrade of tools for

sensing, metering, and measurements at all levels of the grid.

These components will provide the data necessary for monitoring

the grid and the power market. Sensing provides outage detection

and response, evaluates the health of equipment and the integrity

of the grid, eliminates meter estimations, provides energy theft

protection, enables consumer choice, DSM, and various grid

monitoring functions.


Components

Evolution of Electricity Metering


Components

Wide Area Monitoring Systems (WAMS)

WAMS are designed by the utilities for optimal capacity of the

transmission grid and to prevent the spread of disturbances. By

providing real - time information on stability and operating safety

margins, WAMS give early warnings of system disturbances for the

prevention and mitigation of system - wide blackouts. WAMS utilize

sensors distributed throughout the network in conjunction with GPS

satellites for precise time stamping of measurements in the

transmission system. The integrated sensors will interface with the

communication network.


Dr. Majdi Alomari

WAMS Lab Setup RTDS lab setup for wide area system monitoring using hardware PMU

Components


Components

Phasor Measurement Units (PMU)

• Phasor Measurement Units or Synchrophasors give

operators a time - stamped snapshot of the power system. The

PMUs consist of bus voltage phasors and branch current

phasors, in addition to information such as locations and other

network parameters. Phasor measurements are taken with

high precision from different points of the power system at

the same instant, allowing an operator to visualize the exact

angular difference between different locations. PMUs are

equipped with GPS receivers which allow synchronization of

readings taken at distant points.

• The IEEE standard on Synchrophasors specifies the

protocol for communicating the PMU data to the

Phasor Data Concentrator. Dr. Majdi Alomari The Hashemite University (HU)

Dr. Majdi Alomari

Phasor Measuring Unit (PMU)

PMU (Phasor Measurment Unit) derives magnitude and power

angle of the voltage and current with respect to a reference signal,

which is generated from a synchronized GPS clock. As the PMUs

are synchronized to single clock system (GPS), the data collected

will have a common time stamp reference to UTC time. The

collected data will be sending to a phasor data concentrator located

at control centre over IEEE C37.118 communication protocol and

updates to master station at a rate defined by user and measurement

requirement, usually 25,50,10 samples/second.

Components


Dr. Majdi Alomari

Phasor Measuring Unit (PMU)

Components


Dr. Majdi Alomari

Considering the electrical circuit in the

Figure. The computation of ETh and ZTh ,

based on the phasors

VL and IL measured at the load bus, is the

objective

to be pursued. From Kirchoff’s law:

With

According to the phasor diagram in Figure

Phase Measurement Units / PMUs - Phasors Components


Dr. Majdi Alomari

With

Separating in to real and imaginary part

Phase Measurement Units / PMUs - Phasors

Components


Components

Smart Meters

Smart meters have two functions: providing data on energy usage to

customers (end - users) to help control cost and consumption; sending

data to the utility for load factor control, peak - load requirements, and

the development of pricing strategies based on consumption

information and so on Automated data reading is an additional

component of both smart meters and two - way communication

between customers and utilities. The development of smart meters is

planned for electricity, water, and gas consumption.


Dr. Majdi Alomari

Smart Meters

Components


Dr. Majdi Alomari

Smart Meters

Components


Components

Smart Appliances

Smart appliances cycle up and down in response to signals

sent by the utility. The appliances enable customers to

participate in voluntary demand response programs which

award credits for limiting power use in peak demand

periods or when the grid is under stress. An override

function allows customers to control their appliances using

the Internet.


Components

Advanced Metering Infrastructure (AMI) I

AMI is the convergence of the grid, the communication infrastructure,

and the supporting information infrastructure. The network - centric

AMI coupled with the lack of a composite set of cross industry AMI

security requirements and implementation guidance, is the primary

motivation for its development. The problem domains to be addressed

within AMI implementations are relatively new to the utility industry;

however, precedence exists for implementing large - scale, network -

centric solutions with high information assurance requirements.

Serve to reduce/eliminate labor, transportation, and infrastructure

costs associated with meter reading and maintenance, increase

accuracy of billing, and allow for time - based rates while reducing

bad debts; facilitates informed customer participation for energy

management.


Components

Advanced Metering Infrastructure (AMI) II

• Serves to increase customer awareness about load reduction,

reduces bad debt, and improves cash flow, and enhances

customer convenience and satisfaction; provides demand

response and load management to improve system reliability and

performance.

• Curtails customer load for grid management, optimizes network

based on data collected, allows for the location of outages and

restoration of service, improves customer satisfaction, reduces

energy losses, improves performance in event of outage with

reduced outage duration and optimization of the distribution

system and distributed generation management, provides

emergency demand response.


Dr. Majdi Alomari

Advanced Metering Infrastructure (AMI) Components


Dr. Majdi Alomari

Advanced Metering Infrastructure (AMI)

Components


Dr. Majdi Alomari

Advanced Metering Infrastructure (AMI)

Components


Micro Grid

Micro Grid And Smart Grid Comparison

Research has been conducted to understand the differences between a

microgrid and a smart grid. Basically, a microgrid is a local island

grid that can operate as a stand - alone or as a grid - connected

system. It is powered by gas turbines or renewable energy and

includes special purpose inverters and a link for plug - and - play to

the legacy grid. Special purpose filters overcome harmonics problems

while improving power quality and efficiency. In summary, think of

the micro grid as a local power provider with limited advanced control

tools and the smart grid is a wide area provider with sophisticated

automated decision support capabilities Dr. Majdi Alomari The Hashemite University (HU)

Micro Grid

Micro Grid

- Integrate renewable resources.

- Control the flow of power.

- Reduce Carbone emissions.

- Reduce losses.

- Reliable against sudden faults.

- Customer is a part of the grid.


Micro Grid

Grid Micro-Grid


Microgrid Modes

Grid-Connected mode

Island mode

Microgrid

Point of Common Coupling (PCC)

GRID

SWITCH

Micro Grid


Interfacing

Sources

Battery

Grid Loads

INVERTER

Micro Grid


State Estimation

State Estimation

Energy management systems (EMS) run in real time to compute and

maintain security of operation at minimum cost. The power system

measurements provide information to the SE program for processing

and analysis. The functions performed include topology processing

which gathers data about breakers and disconnect switches. State

estimation (SE) of voltage and angles are obtained for all busses using

the weighted least square (WLS) method. Detection of inaccurate data

is obtained from network parameters, tap changing transformers, shunt

capacitors, or breakers.


State Estimation

Detection and Identification of Bad Data

• Redundant measurement information allows us to identify and

locate bad data consisting of gross measurement errors and/or large

modeling errors, for example, wrong network topology, or large

parameter errors. Without redundancy the estimates will fit the

data perfectly, but it does not provide ways to locate bad data.

• The four sources of problems are: gross measurement

errors, small modeling errors, small parameter errors and

inaccurate knowledge of measurement variances.


State Estimation

Pre-Estimation Analysis

The measurement components can undergo a series of so - called

consistency tests with the following objectives:

• Detection of obviously bad measurements.

• Detection of obviously bad network topology.

• Classification of data as (a) valid, (b) suspect, and (c) raw.

• Tuning the measurement variance values.


State Estimation

Detection of Obviously Bad Measurements

In this preliminary stage, measurements whose values are outside

reasonable limits are automatically discarded. For example, line flow

limits can be set at twice the theoretical capacity of the line. Power

factors, voltage levels, and so on can be safely limited. In most cases,

almost all of the bad data will be in this category and can be quickly

discarded.


State Estimation

Normally, a special network configuration program constructs the

system network on the basis of breaker status information. Open lines

are not represented in the model forwarded to the state estimator, that

is, all lines that are in the model used by the estimator should be

closed (energized). However, cases may occur where a breaker is

closed but has an open disconnect switch. If the disconnect switch

status is not reported, the line is mistakenly assumed to be energized.

One way to check for this anomaly is to see if the power flow on the

line is zero at both ends. If this is the case then the line is most

probably open. Other cases of bad topology may be detected from

incoming data and are usually peculiar to the system being analyzed.

Detection of Obviously Bad Network Topology


State Estimation

Classification of Raw Data

Because of the structure of load flow equations a considerable

number of hypothesis consistency tests can be conducted to verify the

validity of most of the data and to tune the values of the measurement

variables.

Line flow measured at both ends: For real flows, the magnitudes of

flows from both ends differ only by the amount of line losses

evaluated. This assures that the combined errors of both

measurements are within the limit.


State Estimation

Post-estimation Analysis I

Post-estimation analysis looks at the results of state and parameter

estimation and attempts to establish hypotheses for the most

probable causes of poor performance, if any. This is based on the

analysis of the normalized measurement residuals defined as:

r ′ i = zi − h(x̂)

ρ i

, i = 1, . . . , m (1)

where ρ2 = var(zi − hi (x̂) i

Statistically, ri′ can vary from zero to three with a high probability.

This is true only when all data lie within the specified statistical accuracies. If there are bad data in the measurements and/or parameters, some of the normalized residual terms will have a large magnitude. In many cases, the measurement with the largest normalized residual is a bad measurement, but this has not been mathematically proven.


State Estimation

Post-estimation Analysis II

Without parameter errors and pre - estimation analysis, all

measurements are classified as raw with a small probability that

some of them are bad. By analyzing the residuals one can start the

hypothesis testing process by discarding the measurement with the

largest residual and performing the estimation process again. If

this fails to yield acceptable estimates, the discarded measurements

are put back and the measurement with the largest residual in the

new estimate is removed. This process is repeated until a

satisfactory answer is obtained.


State Estimation

State Estimation for Smart Grid I

SE is an important tool for detecting and diagnosing errors in

measurement such as network error and/or device malfunctions. The

technique is used in estimating voltage and power flow errors as a

result of system parameters errors.

To solve this, a typical optimization technique could be employed

with the following methodology and algorithm:

• Convergence of the algorithm.

• Clearly defined problem location.

• Manage/modify noise server.

• Time and quality of measurements.


State Estimation

State Estimation for Smart Grid II

Components suggested by SE researchers for the smart grid

include:

1) Real PMU - based measurements to account for integrated

RER and protected load (stochastic) stored.

2) A distributed SE around the different (partitioned) network

configuration on appropriate assessment of data performed

for subsequent assessment.


State Estimation

State Estimation for Smart Grid III


Dr. Majdi Alomari

State Estimation

Current State Estimation For Microgrids


Dr. Majdi Alomari

State Estimation

Current State Estimation For Microgrids


Dr. Majdi Alomari

State Estimation


Dr. Majdi Alomari

x

State Estimation


Dr. Majdi Alomari

State Estimation


Dr. Majdi Alomari

State Estimation

State Estimation


Dr. Majdi Alomari

Weighted Least Square (WLS) Estimator

Most state estimation programs in practical use are formulated as

over determined systems of nonlinear equations and solved as WLS

problems

Problem Formulation :

Consider the nonlinear measurement model:

Zj = hj(x) + ej

Where Zj is the j-th measurement, x is the true state vector, hj(x) is a

nonlinear scalar function relating the j-th measurement to states, and

ej is the measurement error, which is assumed to have zero mean and

variance σ²j. There are m measurements and state variables n < m.

State Estimation


Dr. Majdi Alomari

Weighted Least Square (WLS) Estimator

The WLS state estimation can be formulated mathematically as an

optimization problem with a quadratic objective function and with

equality and inequality constraints

Where rj is the residual, Zj ̶ hj(x) is an objective function, and gi(x),

ci(x) are functions representing power flow quantities. Equality and

inequality constraints are normally used to represent target values

and operating limits in the unobservable parts of the network

State Estimation


Dr. Majdi Alomari

State Variables Complex nodal voltages are the most commonly used variables.

Turn ratios of transformers with taps that change under operating

conditions are also treated as state variables.

Power flows in branches that follow Ohm's law are dependent

variables and can be determined from the state variables (nodal

voltages and turn ratios).

In branches for which application of Ohm's law is not fruitful, such

as branches with unknown impedances, branches with zero

impedances, or closed switches, flows cannot be determined from

these state variables. In these cases one alternative consists in

introducing power flows as additional states.

In the case of open switches, although the flow is known, there is

no relation between the voltage spread across the switch and the zero

power flow; the flow state variable technique is then extended to open

switches. (This helps detecting and identifying bad status information

in switches that are wrongly considered as being open.)

State Estimation


Dr. Majdi Alomari

State Variables Hence, the vector of state variables usually includes the following

states:

State Estimation


Dr. Majdi Alomari

Analog Measurements The following measurements are normally included in practical state

estimators:

State Estimation


Dr. Majdi Alomari

Analog Measurements

State Estimation


Dr. Majdi Alomari

Analog Measurements Not all measurements are obtained simultaneously and hence the

measurements that are obtained in a measurement scan can be time-

skewed up to a few seconds.

In most of the cases, this has no effect on the quality of state

estimates, data inconsistencies may occur when the protection

system is activated or when the system is ramping up or down at a

rapid pace.

In these cases, reliable estimates will be obtained again only when

the system settles down. In this sense, the state estimate is not

actually real time but is only a quasi-static representation of the

conditions in the network.

Truly dynamic, real-time estimation would require time-tagged

measurements obtained via Global Positioning System.

State Estimation


Renewable Resources

Sustainable Energy Options for Smart Grid I

Sustainable energy is derived from natural sources that replenish

themselves over of time. Sometimes called green power, because

they are considered environmentally friendly and socially acceptable,

they include sun, wind, hydro, biomass, and geo - thermal.


Renewable Resources

Sustainable Energy Options for Smart Grid II

Renewable energy options are meant to provide the smart grid with:

1) Remote utilization and storage of RER resources output

2) Enhancement of functionality of grid - connected renewable energy systems (RES)

• Facilitating give - and - take of energy from the system.

• Redistribution/reallocation of unused power from grid -

connected RES

• Facilitating storage of grid - generated and RER - generated

energy by back - up storage technologies at customer end.

• Tracking interactions for billing and study

3) Enhancement of functionality of grid - connected renewable

energy systems (RES)

4) Utilization of vehicle battery packs as energy storage devices


Renewable Resources

Solar Energy

The PV system generally considers:

• Insolation: The availability of solar energy conversion to

electricity. Insolation levels are affected by the operating

temperature of PV cells intensity of light (location - dependent),

and the position of the solar panels (maximize the power tracking

while maximizing perpendicular incident light rays).

• Emission: PV emission levels are environmental friendly.


Renewable Resources

Modelling PV Systems I Many models exist for the calculation of the power output of a PV

cell or bank. Due to the varying efficiencies and numerous

technologies presently available, power output is affected by

environmental conditions and module specifi cations. The I - V

characteristic model of a single cell is commonly used for PV

technologies. The model is given by:

P = Pmp,ref [1 + γ(T − Tref )] mp G

G ref

G is the incident irradiance

Pmp is the maximum power output

Pmp,ref is the maximum power output under standard testing conditions

T is the temperature

Tref is the temperature for standard testing conditions reference (25C )

Gref = 1000Wm2

γ is the maximum power correction for temperature Dr. Majdi Alomari The Hashemite University (HU)

Renewable Resources

Wind Turbine Systems I

Wind is one of the fastest - growing sources of renewable energy

throughout the world. Turbines produce electricity at affordable

cost without additional investments in infrastructure such as

transmission lines. A wind turbine consists of a rotor, generator,

blades, and a driver or coupling device. Compared with PV, wind

is the most economically competitive renewable.

Although turbines produce no CO2 or pollutants, wind has three

drawbacks: output cannot be controlled, wind farms are most

suited for peaking applications, and power is produced only when

there is sufficient wind.


Renewable Resources

Wind Turbine Systems II Machines which consume reactive power and produce real power.

The quantification of the capacity/real power output is given by:


Renewable Resources

Biomass-Bioenergy

• Bioenergy is the energy derived from organic matter such as

corn, wheat, soybeans, wood, and residues that can produce

chemicals and materials. Biopower is obtained from a process

called gasifi cation, converting gas to gas turbines to generate

electricity. Biomass can be converted directly into fluid fuels such

as ethanol, alcohol or biodiesel derived from corn ethanol.


Renewable Resources

Small and Micro Hydropower

• Hydropower is by far the largest renewable source of

power/energy. Small hydropower systems vary from 100 kW to 30

MW while micro hydropower plants are smaller than 100 kW.

Small hydropower generators work in variable speed because of

water flow. Induction motors provide a generator for a turbine

system. The hydraulic turbine converts the water energy to

mechanical rotational energy.

• Small and micro hydropower systems are RER optimizations

to enhance the smart grid. The issues of reliability and modeling

are addressed as in PV and wind energy.


Renewable Resources

Fuel Cell

• Fuel cells can also be used to enhance power delivery in the

smart grid. They are simply fuels from hydrogen, natural gas,

methanol, and gasoline. The efficiency for fuel to electricity can be

high as 65% since it does not depend on Carnot limits. Fuel cells

are environmentally friendly by efficient use of fuel. Fuel cells

produce virtually no emissions. Their cost is significantly high

compared with conventional technologies.

• The topology of a fuel cell is a stack which consists of the part

of the fuel cell that holds the electrodes and electrolytic material.

Hydrogen is extracted from gasoline propane, with natural gas

refineries to operate fuel cells commercially.


Renewable Resources

Heat Pumps

• This form of power is based on accessing the underground

steam or hot water from wells drilled several miles into the earth.

Conversion occurs by pumping hot water to drive conventional

steam turbines which drive the electrical generator that produces

the power. The water is then recycled back into earth to recharge

the reservoir for a continuous energy cycle.

• There are several types of geothermal power plants including

dry steam, flash stream, and binary cycle. Dry steam plants draw

water from the reservoirs of steam, while both flash steam and

binary cycle plants draw their energy from the recycled hot water

reservoir.


Renewable Resources

Electric Vehicles

The integration of electric vehicles and hybrids is another component

of the smart grid system. Vehicle - to - grid power (V2G) uses electric

- drive vehicles (battery, fuel cell, or hybrid) to provide power for

specific electric markets. V2G can provide storage for renewable

energy generation and stabilize large - scale wind generation via

regulation. Hybridization of electric vehicles and connections to the

grid overcome limitations of their use including cost, battery

size/weight, and short range of application.

PHEVs provide the means to replace the use of petroleum - based

energy sources with a mix of energy resources (encountered in

typical electric power systems) and to reduce overall emissions.


Renewable Resources

Electric Vehicles


Renewable Resources

Impact of PHEV on the Grid

• Utilities have become concerned with the number of PHEVs

coming on the market because there may be insufficient supply for

the increased demand resulting from additional load for battery

charging. By 2040, the addition of PHEV battery charging in the

United States will increase existing load by 18%. Unfortunately,

this increase in load will eventually cause voltage collapse in

amounts up to 96% of the nominal voltage in some areas, requiring

the integration of transformers, capacitors, and other power

distribution devices for mitigation.

• It will be critical to study the trends of daily PHEV power usage

and the average power consumption over one day to determine the

impacts on the grid, market, environment, and economy.


Cyber Security

Smart Grid Cyber Security

The interaction of the power, communication, and information

networks are critical to facilitating resiliency and sustainability of the

infrastructures which further enhance the provision of adequate power

and support economic and social growth of the nation. Technologies

and protocols are developed for the maintenance of system, network,

data, and SCADA security while conducting vulnerability assessment,

incident recognition, recording, reporting, and recovery. Protection of

network data as well as web - based or stored data is conducted.


Cyber Security

Standards for the Various Electric Grid Levels


Cyber Security

Threats Facing the Electric Power System



Cyber Security

Improved Interfaces and Decision Support

• The smart grid will require wide, seamless, often real-time use

of applications and tools that enable grid operators and

managers to make decisions quickly.

• Decision support and improved interfaces will enable more

accurate and timely human decision making at all levels of the

grid, including the consumer level, while also enabling more

advanced operator training.

Interfaces and Decision Support


Improved Interfaces and Decision Support

• Advanced Pattern Recognition

• Visualization Human Interface

– Region of Stability Existence (ROSE)

• Real-time calculate the stable region based on the voltage

constraints, thermal limits, etc.

Interfaces and Decision Support


Control Methods and Topologies

• Traditional power system problems:

– Centralized

– No local supervisory control unit

– No fault isolation

– Relied entirely on electricity from the grid



IDAPS: Intelligent Distributed Autonomous Power Systems

• Distributed

• Loosely connected APSs

• Autonomous

– Can perform automatic control without human intervention,

such as fault isolation

• Intelligent

– Demand-side management

– Securing critical loads



• A localized group of electricity sources and loads

– Locally utilizing natural gas or renewable energy

– Reducing the waste during transmission

Using Combined Heat and Power (CHP)

APS: Autonomous Power System



Multi-Agent Control System

• IDAPS management agent

– Monitor the health of the system and perform fault isolation

– Intelligent control

• DG agent

– Monitor and control the DG power

– Provide information, such as availability and prices

• User agent

– Provide the interface for the end users



IDAPS Agent Technology Control Methods and Topologies


IDAPS Agent Technology

• Securing critical

loads



IDAPS Agent Technology

• Demand-side

management



Quantifying Necessary Generation to Secure

Critical Loads

• Non-linear optimization model

– Minimize the total annual levelized capital and operating

costs of the candidate generators.

– Subject to

• Reliability constraints.

• Maximum size of each technology.

• Maximum number of units to be installed.

• The annual emission caps for CO2, NOx, and SOx



Test Case



Electricity Supply Candidates



Solutions for Reliability

Improvement

LOLP: Loss of load probability

52 minutes per year Control Methods and Topologies


Value of DG for Peak Shaving



Diverse Energy Sources

Wind

Solar

Nuclear

Fossil

Trading in Smart Grid


Electricity Market

• Current practice: Fixed market

– Few producers, less competition

– Regulated by government

• The future : Free market

– Many producers (wind, solar, …)

– Less regulation

“Trading Agents for the Smart Electricity Grid,” AAMAS 2010.



Goal

• Setup a Electricity market

– Self interested (producer, buyer, grid owner).

– Free (no central regulation) .

– Efficient (no overload, no shortage).



Design

• Trading Mechanism

– Buy/sell electricity.

• Overload Prevention Mechanism

– Transmission charge.

• Online Balancing Mechanism

– Price for extra demand and supply in real-time.



Stock Market

Buy orders Sell orders

• Market order : buy or sell at market price

• Limit order : specify price to sell or buy



Proposed Electricity Trading

Price Quantity A day ahead electricity market

• A day ahead market

– Based on prediction of a day ahead demand/supply



Overload Prevention Mechanism

• Charging transmission (line charge = pt)

– Protect overload because

• If pt is high then demand goes down

• If pt is low then demand goes high

– Line charge is geographically different depending on

congestion



Online Balancing Mechanism

• Balancing unpredictable demand/supply on real-time basis

+ demand

• need to buy at market price

- demand

• Need to sell at market price

- supply

• Buyer need to buy at market price



Evaluation

• How efficient the market is?

• What’s the best trading strategy?



Market Efficiency

• Efficient-market hypothesis (EMH)

– If all information (buyer’s and seller’s cost structure) is publicly

available.

– Market price is determined solely by supply/demand

• maximally efficient market.

• Cost structure

– Buyer : minimum and cost sensitive dynamic demand.

– Seller : minimum and quantity proportional production cost.

– Line owner : minimum and quantity proportional cost.



Trading Strategy

• Maximum efficiency is not possible

– Hidden cost information

– Line charge constraint

• ZI

– Random pricing

• AA-EM

– Follow the market price but weighted

• Bias to the same node due to line charging.



Market Efficiency

• With respect to capacity

Average Transmission Line Capacity (log-scale)

Eff

icie

ncy




Documents

Smart Grids - Electricals 4 Youe4uhu.com/down/smart grid technology/Sildes ( Dr.Majdi ).pdf · Smart Grids Definitions II In “Smarter Grids: The Opportunity”, the Smart Grid is